Field
The present invention relates to monitoring vital signs of a subject and especially to a system, method and a computer program product for monitoring a level of stress of a subject.
Description of the Related Art
Stress may be defined as a state of bodily or mental tension resulting from factors that tend to after an existent equilibrium (Merriam-Webster's Online Dictionary). In recent years, it has become increasingly important both from wellness and from athletic training point of view to have a good and reliable picture of the level of one's physiological and/or mental stress, and recovery of it. This may be especially important for optimal training in endurance sports.
Heart rate variability (HRV) refers to a variation in the beat-to-beat interval of the heart. Variation in the beat-to-beat interval is a physiological phenomenon; the sinoatrial node of the heart receives several different inputs, and the instantaneous heart rate and its variation are results of these inputs. Recent studies have increasingly linked high HRV to good health and a high level of fitness, whilst decreased HRV is associated to stress and tiredness. In various applications, stress and recovery from stress has been thus estimated by measuring HRV from beat-to-beat intervals of the heart.
Many stress and recovery monitoring applications use an ECG (electro cardiogram) technique to measure beat-to-beat intervals of the heart of a subject, and HRV is determined from these measured values. It is known that a naturally occurring primary fluctuation in the heart rate occurs because of breathing, but despite many studies, precise mechanisms of respiration-induced heart rate variations are still not fully known. The monitoring applications include various HRV analysis methods, but many challenges still exist.
An example of a situation where an error may occur is overnight recovery analysis, e.g. for athletes. FIG. 1 illustrates heart rate and heart rate variability of a person in an overnight measurement. It may be seen that during the phase of falling asleep the depth of respiration and the variability of respiration rate decrease, and along with them also the heart rate variability decreases. This tends to lead to an erroneous judgment of increased stress, although the opposite happens.
To make individual results comparable, some simple commercial training applications instruct their users to measure HRV daily in similar conditions (e.g. in the morning). This eliminates some of the effect of respiration to the measurements, but similarity of relevant conditions is difficult to verify, and therefore the results are reliable only to an extent. In other type of training applications, users are requested to provide manually input additional data related to their pre-measurement activity, and this additional data is used to improve interpretation of the measured HRV values. Such methods are more accurate, but laborious to the users. Furthermore, they are still indirect, i.e. the interpretation is based on experimental and averaged statistical data.
There are also applications that use advanced mathematical analysis methods to determine the level of stress from measured beat-to-beat intervals. For example, frequency-domain methods assign bands of frequency and divide measured beat-to-beat intervals to them. Stress level is then derived from distribution of the measured intervals across these frequency bands. In time-domain methods beat-to-beat intervals are statistically analyzed to give variables, such as standard deviation, root mean square of successive differences, etc. These methods may provide more accuracy, but require a lot of computing. And still, interpretation of the computed distributions and variables is indirect, i.e. based on experimental and averaged data.